Abstract

Electroencephalography (EEG) and near-infrared spectroscopy (NIRS) are non-invasive neuroimaging methods that record the electrical and metabolic activity of the brain, respectively. Hybrid EEG-NIRS brain-computer interfaces (hBCIs) that use complementary EEG and NIRS information to enhance BCI performance have recently emerged to overcome the limitations of existing unimodal BCIs, such as vulnerability to motion artifacts for EEG-BCI or low temporal resolution for NIRS-BCI. However, with respect to NIRS-BCI, in order to fully induce a task-related brain activation, a relatively long trial length (≥10 s) is selected owing to the inherent hemodynamic delay that lowers the information transfer rate (ITR; bits/min). To alleviate the ITR degradation, we propose a more practical hBCI operated by intuitive mental tasks, such as mental arithmetic (MA) and word chain (WC) tasks, performed within a short trial length (5 s). In addition, the suitability of the WC as a BCI task was assessed, which has so far rarely been used in the BCI field. In this experiment, EEG and NIRS data were simultaneously recorded while participants performed MA and WC tasks without preliminary training and remained relaxed (baseline; BL). Each task was performed for 5 s, which was a shorter time than previous hBCI studies. Subsequently, a classification was performed to discriminate MA-related or WC-related brain activations from BL-related activations. By using hBCI in the offline/pseudo-online analyses, average classification accuracies of 90.0 ± 7.1/85.5 ± 8.1% and 85.8 ± 8.6/79.5 ± 13.4% for MA vs. BL and WC vs. BL, respectively, were achieved. These were significantly higher than those of the unimodal EEG- or NIRS-BCI in most cases. Given the short trial length and improved classification accuracy, the average ITRs were improved by more than 96.6% for MA vs. BL and 87.1% for WC vs. BL, respectively, compared to those reported in previous studies. The suitability of implementing a more practical hBCI based on intuitive mental tasks without preliminary training and with a shorter trial length was validated when compared to previous studies.

Highlights

  • A hybrid brain-computer interface (BCI) refers to a BCI system that combines two or more different types of brain signals or one brain signal with another bio-signal, such as electrooculogram (EOG) or electromyogram (EMG) [1]

  • The suitability of implementing a more practical Hybrid EEG-NIRS brain-computer interfaces (hBCIs) based on intuitive mental tasks without preliminary training and with a shorter trial length was validated when compared to previous studies

  • Hybrid BCIs provide a higher classification accuracy than those of unimodal BCIs based on electroencephalogram (EEG), near-infrared spectroscopy (NIRS) [5], or functional magnetic resonance imaging [6,7,8]

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Summary

Introduction

A hybrid brain-computer interface (BCI) refers to a BCI system that combines two or more different types of brain signals or one brain signal with another bio-signal, such as electrooculogram (EOG) or electromyogram (EMG) [1]. Hybrid BCIs provide a higher classification accuracy than those of unimodal BCIs based on electroencephalogram (EEG), near-infrared spectroscopy (NIRS) [5], or functional magnetic resonance imaging (fMRI) [6,7,8]. They can simultaneously employ the advantages of different modalities, such as a high temporal resolution of EEG, robustness to the physiological artifact of NIRS, and high spatial resolution of fMRI [9,10]. Several researchers have focused on hybrid BCIs and introduced a variety of interesting hybrid BCI systems [11,12,13,14,15,16,17,18]

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